Rumination on systemic economic and social change

Monthly Archives: January 2012

This blog post is about what I see as one of the most important papers linking the complexity sciences to development and humanitarian efforts – at least it is for me personally, but I think it also takes a very important position in the discussion in general.

The paper has the title ‘Exploring the science of complexity: Ideas and implications for development and humanitarian efforts’ and is authored by Ben Ramalingam (author of the blog Aid on the Edge of Chaos) and Harry Jones with Toussaint Reba and John Young. The paper can be downloaded here.

Why do I think is the paper so important? For me personally it was the first paper I read that explicitly linked the two domains (complexity science and international development) and it does that in a very comprehensive and systematic manner.

Ramalingam and colleagues go back to the origins of complexity sciences and put it into context by showing applications in the social, political and economic realms. They unpack the complexity sciences and present them in ten key concepts divided into three sets, i.e., complexity and systems, complexity and change, and complexity and agency. Here an overview taken from p 8. of their paper:

Complexity and systems: These first three concepts relate to the features of systems which can be described as complex:

Systems characterised by interconnected and interdependent elements and dimensions are a key starting point for understanding complexity science.

Emergence describes how the behaviour of systems emerges – often unpredictably – from the interaction of the parts, such that the whole is different to the sum of the parts.

Complexity and change: The next four concepts relate to phenomena through which complexity manifests itself:

Within complex systems, relationships between dimensions are frequently nonlinear, i.e., when change happens, it is frequently disproportionate and unpredictable.

Sensitivity to initial conditions highlights how small differences in the initial state of a system can lead to massive differences later; butterfly effects and bifurcations are two ways in which complex systems can change drastically over time.

Phase space helps to build a picture of the dimensions of a system, and how they change over time. This enables understanding of how systems move and evolve over time.

Chaos and edge of chaos describe the order underlying the seemingly random behaviours exhibited by certain complex systems.

Complexity and agency: The final three concepts relate to the notion of adaptive agents, and how their behaviours are manifested in complex systems:

Adaptive agents react to the system and to each other, leading to a number of phenomena.

Self-organisation characterises a particular form of emergent property that can occur in systems of adaptive agents.

Co-evolution describes how, within a system of adaptive agents, co-evolution occurs, such that the overall system and the agents within it evolve together, or co-evolve, over time.

In great detail they explain every concept, give examples and discuss the implications of the concepts for the development system.

I like the paper because it really brings together all those important concepts in an accessible way. Although the paper is pretty long (89 pages all in all), it is not at all a boring read. In the conclusion part of the paper, the authors also describe the difficulty of presenting such an intricate matter as complexity sciences, itself being not a unified scientific discipline:

[…] it is useful to note that scientific knowledge is usually characterised with reference to the metaphor of a building. The ease with which the terms ‘foundations’, ‘pillars’ and ‘structures’ of knowledge are used indicates the prevalence of this architectural metaphor. Our difficulty was in trying to represent complexity science concepts as though they were parts of a building. They are, in fact, more like a loose network of interconnected and interdependent ideas. A more detailed look highlights conceptual linkages and interconnections between the different ideas. The best way to see how they fit together in the development and humanitarian field would be to try to apply them to a specific challenge or problem. […] Based on our reading, however, a grand edifice may never be erected along the lines of, for example, neoclassical economics. If this is the case, it may be that we need to become better accustomed to a network-oriented model of how knowledge and ideas relate to each other.

For me, it is intriguing how the science of complexity not only defies scientific practices by diverting from the pure deductive and inductive approaches and combining them but also evaded characterizations in ‘traditional’ scientific schemes such as the building mentioned above. This reminds me of the book ‘Complexity and Postmodernism’ by Paul Cilliers, which I started reading but I got stuck somewhere in the middle, overwhelmed by his theory and language. I hope that I will finish it some day and report on that here.

The authors also try to answer a number of questions around the topic of the application of complexity to development and what it means for example for international donors. A few quotes from the concluding remarks:

In our view, the value of complexity concepts are at a meta-level, in that they suggest new ways to think about problems and new questions that should be posed and answered, rather than specific concrete steps that should be taken as a result.

[…]

As well as use by implementing agencies, an understanding of complexity must also be built into the frameworks of the donors and others who hold the power to determine the shape of development interventions. This may be easier said than done – complexity requires a shift in attitudes that would not necessarily be welcome to many working in Northern agencies. For example, such a shift may require adjusting away from the ‘mechanistic’ approach to policy, or being prepared to admit that most organisations are learning about development interventions as they go along, or being transparent about the fact that taxpayers’ money may be spent on a project that does not guarantee results. It may mean having smaller, but better programmes.

[…]

At the start of our exploration, our view was simply that complexity would be a very interesting place to visit. At the end, we are of the opinion that many of us in the aid world live with complexity daily. There is a real need to start to recognise this explicitly, and try and understand and deal with this better. The science of complexity provides some valuable ideas. While it may be impossible to apply the complexity concepts comprehensively throughout the aid system, it is certainly possible and potentially very valuable to start to explore and apply them in relevant situations.

To do this, agencies first need to work to develop collective intellectual openness to ask a new, potentially valuable, but challenging set of questions of their mission and their work. Second, they

need to work to develop collective intellectual and methodological restraint to accept the limitations of a new and potentially valuable set of ideas, and not misuse or abuse them or let them become part of the ever-swinging pendulum of aid approaches. Third, they need to be humble and honest about the scope of what can be achieved through ‘outsider’ interventions, about the kinds of mistakes that are so often made, and about the reasons why such mistakes are repeated. Fourth, and perhaps most importantly, they need to develop the individual, institutional and political courage to face up to the implications.

I’d recommend anyone who works in international development and is interested in complexity to read this paper. It is a perfect entry point also for people with no background in complexity science.

I recently stumbled over a blog called Complexity Finance by a company called Rational Investment. A series of three posts which I liked was called ‘What ants can teach us about the market’. In part one, the author writes about a phenomenon that the number of ants would, given two identical and steadily replenished food sources not be divided 50/50, but rather 80/20:

Alan Kirman found some interesting behavior in the foraging activities of ants. He starts his account by citing the results of an experiment by Deneuboug et al. (1987a) and Pasteels et al. (1987) where two identical food sources were offered to ants. They were replenished so that they remained identical. Ants, after a period of time, were found not to be split 50/50 as common sense would conclude, but rather 80/20. Kirman further noted that this 80/20 split would often reverse inexplicably.[1] This phenomenon is mirrored in studies by Becker where only one of two similar restaurants on opposite sides of the street tend to attract long lines of customers.

Apparently, this behavior is also mirrored by investors in a market.

In part two of the series, the author introduces Melanie Mitchell’s book on complexity and especially what she writes on ants. I introduced the book in an earlier post.

In part three, another interesting concept is introduced: herding. Herding was identified as a common behavior in markets, responsible for creating trends.

Described as “History’s Hidden Engine”, socionomics posits that large trends in society and the market are driven by social mood. If the society at large is feeling positive, constructive behavior ensues, e.g. cooperation between governments, a rising stock market, expanding economy, box-shaped cars and brighter fashion tones. A negative mood will cause society to go to war, the stock market to decline, a recession/depression, rounder-shaped cars and darker fashion tones.

Socionomics is counter-intuitive in that most people believe events cause social mood. The stock market goes up and investors feel happy. Socionomics believes that a society that feels happy, for whatever prior cause, will cause them to buy stocks. It is the mood that causes the event. This mood is generated and reinforced through the herding mechanism.

Herding behavior is simply acting the way others do. It is a type of sampling heuristic and, like cognitive biases, is triggered in times of uncertainty. When uncertain about what to do, most will default to following the actions of others. The socionomic model of herding describes it as “a model of unconscious, prerational herding behavior that posits endogenous dynamics that have evolved in homogenous groups of humans in contexts of uncertainty, while eschewing the traditional economic assumptions of equilibrium and utility-maximization.”

I wonder how this herding behavior could be used in the work of developing markets for the poor in developing countries. I do recognize one type of herding in these contexts that I often don’t see as particularly helpful, but a very understandable behavior: all people in a region, market, village, etc. do the same thing, regardless whether it is particularly beneficial or profitable. In general, diversification would not only lead to higher profits by tapping new markets, but also to a higher degree of resilience by not depend on only one product. A negative instance of herding?

Maybe the increasing interest of companies (and investors?) in social business can be seen as a positive type of herding that needs to be better exploited.

My friend Shawn Cunningham sent me an email with the visualization of his LinkedIn network and I was so fascinated that I had to see my network. The tool that does the visualization is called LinkedIn Maps. You can click on the map to enlarge it. Here is a link if you want to do your own network.

What’s the most fascinating thing about the map? Well, for one it shows the power of visualization. Individual parts of the network are shown in different colors and you can label them. I found networks that are composed of people I know from my work in the field of market facilitation, others from my work in Bangladesh or professional colleagues in Switzerland. That’s not really a new insight, but the visualization just makes it so much more clear and accessible.

This is exactly why I like causal loop diagrams. Although they might not be able to be used as model to predict how a system is behaving, they still help us to understand the structure of a system.

Another thing I can find on the visualization of the network are connections between people I haven’t known that they were connected. Also the network structure is interesting. Especially the blue network in the bottom has a clear hub, the other networks are more distributed.

I haven’t really done a lot of work in network theory but it is definitely a field that interests me since it is so strongly connected to (or even part of) complexity sciences. I shall take more time to read.